{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. 什么是CAPM模型？\n",
    "*Alpha与Beta模型*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. Alpha与Beta分别是什么？\n",
    "2. CAPM是什么？\n",
    "3. 如何计算股票与大盘的相关性？\n",
    "4. 如何用图形显示CAPM模型？\n",
    "5. 如何对模型做方差分析？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alpha与Beta分别是什么？\n",
    "\n",
    "Alpha指的是投资者所获得的超额回报，即高于或低于其业绩基准的市场指数。\n",
    "Beta是一种衡量风险的方法，可以应用于个股或股票投资组合。Beta系数较高的股票通常会对市场或宏观经济新闻具有更加不稳定的波动。\n",
    "\n",
    "### Beta算法：\n",
    "\n",
    "$B_x = \\frac{\\sigma_{x,m}}{\\sigma^2_m}$\n",
    "\n",
    "$P_{x,m} = \\frac{\\sigma_{x,m}}{\\sigma_x\\sigma_m}$\n",
    "\n",
    "$B_x=p_{x,m}*\\frac{\\sigma_x}{\\sigma_m}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CAPM模型是什么？\n",
    "CAPM模型公式：\n",
    "\n",
    "$R_x-R_f=a_x+B_x(R_m-R_f)+\\epsilon$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何计算股票与大盘的相关性？\n",
    "协方差（Covariance）:\n",
    "\n",
    "$cov(X,Y) = \\frac {\\sum_{i=1}^n(X_i-\\overline X)^2(Y_i-\\overline Y)^2}{n-1}$\n",
    "\n",
    "相关系数（Correlation）：\n",
    "\n",
    "$cor(X,Y) = \\frac {Cov(X,Y)}{\\sigma X \\sigma Y}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            asset1_r  asset2_r  benchmark_r\n",
      "date                                       \n",
      "2016-01-05  0.008840 -0.004043    -0.002594\n",
      "2016-01-06  0.011434  0.004684     0.022548\n",
      "2016-01-07 -0.051136 -0.036984    -0.070450\n",
      "2016-01-08  0.016368  0.008391     0.019651\n",
      "2016-01-11 -0.032318 -0.045830    -0.053261\n",
      "Correlation coefficients\n",
      "000001 and benchmark:  0.878683134657\n",
      "600036 and benchmark:  0.57832179532\n",
      "000001 and 600036:  0.686406557927\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import tushare as ts\n",
    "\n",
    "asset1 = ts.get_k_data('000001', start='2016-01-01', end='2016-12-31', ktype='D',autype='qfq')\n",
    "asset1.index = pd.to_datetime(asset1['date'], format='%Y-%m-%d')\n",
    "asset1_r = asset1['close'].pct_change()[1:]\n",
    "\n",
    "asset2 = ts.get_k_data('600036', start='2016-01-01', end='2016-12-31', ktype='D',autype='qfq')\n",
    "asset2.index = pd.to_datetime(asset2['date'], format='%Y-%m-%d')\n",
    "asset2_r = asset2['close'].pct_change()[1:]\n",
    "\n",
    "benchmark = ts.get_hist_data('sh', start='2016-01-01', end='2016-12-31', ktype='D')[::-1]\n",
    "benchmark.index = pd.to_datetime(benchmark.index, format='%Y-%m-%d')\n",
    "benchmark_r = benchmark.close.pct_change()[1:]\n",
    "\n",
    "new = pd.concat([asset1_r, asset2_r, benchmark_r], join='inner', axis=1)\n",
    "new.columns = ['asset1_r', 'asset2_r', 'benchmark_r']\n",
    "\n",
    "print new.head()\n",
    "\n",
    "\n",
    "print \"Correlation coefficients\"\n",
    "print \"000001 and benchmark: \", np.corrcoef(new['asset1_r'],new['benchmark_r'])[0,1]\n",
    "print \"600036 and benchmark: \", np.corrcoef(new['asset2_r'],new['benchmark_r'])[0,1]\n",
    "print \"000001 and 600036: \", np.corrcoef(new['asset1_r'],new['asset2_r'])[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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v3S8zc3Xdt++FVQF6cemknnj2WNcNb8r+NwAAecWUTgDog7KvcQqb0mfS6evbpzmGrWtL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\n068nrQxYG3+hqn3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5f605c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "plt.scatter(new['asset2_r'], new['benchmark_r'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.505352553688\n"
     ]
    }
   ],
   "source": [
    "## Beta值计算\n",
    "Beta_asset2 = np.corrcoef(new['asset2_r'],new['benchmark_r'])[0,1]*new['asset2_r'].std()/new['benchmark_r'].std()\n",
    "print Beta_asset2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用图形显示CAPM模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>asset1_r</td>     <th>  R-squared:         </th> <td>   0.772</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.771</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   816.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 17 Jun 2017</td> <th>  Prob (F-statistic):</th> <td>2.39e-79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:51:14</td>     <th>  Log-Likelihood:    </th> <td>  917.83</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   243</td>      <th>  AIC:               </th> <td>  -1832.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   241</td>      <th>  BIC:               </th> <td>  -1825.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>       <td> 9.949e-05</td> <td>    0.000</td> <td>    0.279</td> <td> 0.781</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>benchmark_r</th> <td>    0.7358</td> <td>    0.026</td> <td>   28.573</td> <td> 0.000</td> <td>    0.685</td> <td>    0.787</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>26.382</td> <th>  Durbin-Watson:     </th> <td>   2.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  67.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.462</td> <th>  Prob(JB):          </th> <td>1.81e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.419</td> <th>  Cond. No.          </th> <td>    72.2</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               asset1_r   R-squared:                       0.772\n",
       "Model:                            OLS   Adj. R-squared:                  0.771\n",
       "Method:                 Least Squares   F-statistic:                     816.4\n",
       "Date:                Sat, 17 Jun 2017   Prob (F-statistic):           2.39e-79\n",
       "Time:                        13:51:14   Log-Likelihood:                 917.83\n",
       "No. Observations:                 243   AIC:                            -1832.\n",
       "Df Residuals:                     241   BIC:                            -1825.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "const        9.949e-05      0.000      0.279      0.781      -0.001       0.001\n",
       "benchmark_r     0.7358      0.026     28.573      0.000       0.685       0.787\n",
       "==============================================================================\n",
       "Omnibus:                       26.382   Durbin-Watson:                   2.012\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               67.894\n",
       "Skew:                           0.462   Prob(JB):                     1.81e-15\n",
       "Kurtosis:                       5.419   Cond. No.                         72.2\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "model = sm.OLS(new['asset1_r'], sm.add_constant(new['benchmark_r']))\n",
    "\n",
    "result = model.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mFSs32y0hncew5Ugnn/nNO6w7MLxJa23HCeg2mMRy+NQx9M5X9lPd5uHuL5xB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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x224ba860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CAPM = result.params[0] + result.params[1]*new['benchmark_r']\n",
    "Diff = new['asset2_r'] - CAPM \n",
    "\n",
    "fig = plt.figure(figsize=(15, 7))\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(CAPM, label='CAPM')\n",
    "plt.plot(new['asset2_r'], label='600036_r')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(2,1,2)\n",
    "plt.bar(new.index, Diff)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何对模型做方差分析？\n",
    "### 单因素方差分析：\n",
    "\n",
    "计算单大盘指数的因素(PR)对股票收益率的影响是否显著。\n",
    "\n",
    "### 析因素方差分析：\n",
    "\n",
    "计算各因素对股票收益率的影响(PR)是否显著，并计算其他因子间的影响是否有显著依赖。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                df    sum_sq   mean_sq          F        PR(>F)\n",
      "benchmark_r    1.0  0.011906  0.011906  121.08287  4.380746e-23\n",
      "Residual     241.0  0.023698  0.000098        NaN           NaN\n",
      "                         df    sum_sq   mean_sq           F        PR(>F)\n",
      "asset1_r                1.0  0.016777  0.016777  216.879682  2.278162e-35\n",
      "benchmark_r             1.0  0.000097  0.000097    1.251745  2.643427e-01\n",
      "asset1_r:benchmark_r    1.0  0.000241  0.000241    3.120282  7.859988e-02\n",
      "Residual              239.0  0.018489  0.000077         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "# 单因素分析\n",
    "var_model = ols('asset2_r~benchmark_r', data=new).fit()\n",
    "table = anova_lm(var_model)\n",
    "\n",
    "#析因素分析\n",
    "var_model1 = ols('asset2_r~asset1_r*benchmark_r', data=new).fit()\n",
    "table1 = anova_lm(var_model1)\n",
    "\n",
    "print table\n",
    "print table1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "用其他股票做CAPM模型"
   ]
  }
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